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10 results for “regional economy”
Figure 2. from: Unicorn–Open science for assessing environmental state, human health and regional economy - Research Ideas and Outcomes 2: e9232 (16 May 2016) https://doi.org/10.3897/rio.2.e9232
Figure 2. - Time line of the tasks in UNICORN-project
Figure 1. from: Unicorn–Open science for assessing environmental state, human health and regional economy - Research Ideas and Outcomes 2: e9232 (16 May 2016) https://doi.org/10.3897/rio.2.e9232
Figure 1. - Links and interactions between the work packages
Database of Rural Technological Trajectories, their variants and territories featured by peasantries of the Brazilian Northern Region based on Agricultural Censuses and special tabulations for the Economy of Agroforestry Systems (2006 and 2017)
<p><strong>Introduction</strong></p> <p>This database contains the string variables at municipal level that qualifies the techno-productive trajectories (TT) of the Brazilian Northern Region Agrarian Economy, their technological variants (TTV) and territories based on peasantries. The TTs and TTVs were defined and theoretically justified by Costa (2021, p. 217-219).</p> <p><strong>Delimitation of Technological Trajectories, their variants and territories featured by peasantries</strong></p> <p>The TTs are designed by a method that combines <em>differentiation and structural signification</em> of rural production in each territory – hereafter, Method of Differentiation and Structural Signification of Rural Production (M-DESTRU).</p> <p><em>Structural differentiation</em> (Phase 1) is necessary because production systems activities play different roles, depending on the systems production modes and their territorial context: cattle ranching, for example, performs very different economic functions when practiced in family structures (peasants) in the municipalities of the Lower Amazonas, in comparison with wage-based farms in Southeast Pará; the roles played by temporary crops in the peasant systems of the Lower Tocantins are also quite different from those that are observed among employers' establishments in the Lower Amazon; and so on. This phase of the methodology qualifies these differences and has its procedures described on pages 440 and 441 of Costa (2021).</p> <p>In phase 2, M-DESTRU verifies how these structurally dissimilar activities, combine with others linked to the practices of the agents of each production mode, conforming convergences that result in distinct patterns. These patterns are semantically associated with TTs or TTPs<em> structures</em> that are in movement, and these structures all together make up for the region's rural economic system. This Phase's procedures are detailed on pages 441 and 442 of the aforementioned work. The codes of variable “Technological Trajectories” in this database are “CamponêsT1” for “Peasant Trajectory.T1” in Costa, 2021; “CamponêsT2” for “Peasant Trajectory.T2”; “CamponêsT3” for “Peasant Trajectory.T3”; “PatronalT4” for “Employer.T4”; “PatronalT5” for “Employer.T5”; “PatronalT7” for “Employer.T7”.</p> <p>In turn, the procedures to get the technological variants of TTs (TTVs) for census years 2006 and 2017 are described in Costa, 2021, p. 447-451. The codes of variable “Technological Variants” in this database are “IQ” for “CI = Chemical Intensity” in Costa, 2021; “IM” for “MI = Mechanical Intensity”; “IT” for “LI = Labour Intensity”; “IPst” for “PI = Pasture Improvement”; “IReb” for “HI = Herd improvement”; “Crg” for “LoadC=Load Capacity of Pasture”; “SAF-F” for “AFSs-F = AFSs with the presence of forest management”; “SAF-A” for “AFSs-A = Artificially developed AFSs”; “+” after the attribute for “Attribute clearly verified; “–“ for “Attribute clearly absent”; “0” for “an uncertain attribute”.</p> <p>The Brazilian Northern Region encompasses the municipalities of the federative states Acre, Amapá, Amazonas, Mato Grosso, Pará, Rondônia, Roraima and Tocantins. A municipality is codified by the variable “Peasantry” as “ACaboclo_Originário”, meaning a territory of an “original caboclo peasantry (OcP in English or CbO in Portuguese)”, if founded before 1880; “BCaboclo_Forâneo”, meaning a territory of an ”immigrant caboclo peasants (IcP or CbF)”, if founded between 1880 and 1910; “CAgrícola_Forâneo, meaning a territory of a “post-ruber immigrant agricultural peasantry (IpR or FpB)”, if founded between 1910 and 1960; and “DContemporâneo”, meaning a “recent peasantry (ReP or ReC)”, if founded since 1960.</p> <p>The base data are from the Brazilian Institute of Geography and Statistics (IBGE), from the 2006 and 2017 Agricultural Censuses. The following special cases were handled:</p> <ul> <li>In the Agricultural Census 2017 credit data were not available. However, the Central Bank of Brazil informs for that year total rural credit for family-based and non-family-based agriculture and livestock by municipality.</li> <li>Comparing the production of manioc in the 2006 census with the production of manioc flour in the same year and with the historical production, we arrived at errors in three municipalities in Pará: in Moju a manioc production of 498,907 t is recorded adding the two sets of data (Peasant and Employer), when in fact it is 42,132; in São Miguel do Guamá the figure of 392,784 t is recorded when it actually is 175,941; 204,216 is recorded in Viseu and 106,287 is actual figure. Corrections were made using the proportion manioc/manioc flour prevailing in other municipalities in same microregion.</li> <li>In the census there are the value of the production of manioc and the value of the production of manioc flour. Since we are dealing with the same producer, if we consider in gross value of production or income aggregations both products, we incur double counting. In such cases, the value related to manioc flour was considered.</li> <li>The 2006 census has information on fishing restricted to the monetary income from the sale of fish, as a complementary income variable. The information does not incorporate the value of fish consumed in the establishment. Therefore, it is not a variable equivalent to the GVP of all other products considered. In turn, the 2017 census provides data on fish production as part of livestock (the gross value of fish production in captivity, which makes up the gross value of livestock production) but does not maintain the fish sales variable from the previous census. This income is contained in the variable “other producer income”, in which, none of the other possibilities listed (esgargot, etc.) are adhered to T2 (IBGE, Censo Agropecuário de 2017. Rio de Janeiro, IBGE, 2018). Therefore, two things were done to incorporate fisheries: a) for 2006 the GVP of fishery production was considered the variable "fish sale" under the heading "other producer income" divided by 1 minus the self-consumption rate 31% (Costa et al, 2022); b) for 2017, the GVP of fisheries production resulted from the division of the variable “other producer's incomes” by the same denominator of the operation described in “a”.</li> </ul> <p><strong>The dataset is organized as:</strong></p> <p>1. Data set with variables delimiting TT, TTV and Peasantry</p> <p><em>2006_NorthRegion_TechVariants.csv</em><br> <em>2017_ NorthRegion_TechVariants.csv</em>.</p> <p>In each table the column names are self-explanatory.</p> <p>2. Dataset with special tabulation or the agroforestry systems economy represented by Peasant Trajectory.T2</p> <p><strong> Gross Value of Production</strong></p> <p><em> Table1_NorthRegion_T2_GVP.csv <br> </em>Table 1a – Gross Value of Productios (GVP) by products of AFSs-F and peasantry 2006 and 2017<br> Table 1b – Gross Value of Productios (GVP) by products of AFSs-A and peasantry, 2006 and 201<br> Table 1c – Gross Value of Productios (GVP) by products of T2, technological variant, and peasantry, 2006 and 2017 </p> <p><strong> Real Product</strong></p> <p> Real Product” (RP): For each year (i), the vector of produced quantities (Qi) multiplied by a vector of fixed prices (P1): variation of RP is explained exclusively by the variation of Q. <br> <em>Table2_NorthRegion_T2_RealProduct.csv</em><br> Table 2a – T2 Production by technological variant and peasantry, 2006<br> Table 2b – T2 Production Value (=Real Product) by technological variant, and peasantry, 2006 in R$ 1,000 currents<br> Table 2c – T2 Implicit prices by technological variant, and peasantry, 2006, R$ 1.000 currents (each cel in Table 2b divided by corresponding cel in Table 2a)<br> Table 2d – T2 Production by technological variant, and peasantry, 2017<br> Table 2e – T2 Real Product1 by technological variant, and peasantry, 2017 in R$ 1,000 from 2006 (each cel in Table 2c multiplied by corresponding cel in Table 2d)</p> <p><strong> Key variables</strong><br> <em> Table3_NorthRegion_T2_KeyVariables.csv</em><br> Table 3 – Key variables of T2 economy by peasantry, 2006 and 2017</p> <p><strong>Reference:</strong></p> <p>Costa FA. 2021. Structural diversity and change in rural Amazonia: A comparative assessment of the technological trajectories based on agricultural censuses (1995, 2006 and 2017). Nova Economia 31(2).</p> <p>COSTA, F. A., FEIJÃO,, L. G., ALMEIDA, I. C., NOGUEIRA, K. N. S., AMERICO, M. C. (2022). Database of a Riverine Economy in Mocajuba, Low Tocantins, Pará, Amazonia, Brazil [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7121336</p>
Multi-regional Input–output Table for the Global Emerging Economies (EMERGING)
<p><strong>EMERGING</strong>: Up-to-date and full-scale MRIO tables covering 135 sector code (the actual number of sectors is 134) in 245 economies. The EMERGING database is also designed to incorporate more official and publicly available data from national statistical institutes to ensure a high level of data quality, especially for these economies. </p> <p>Please see the steps in the development of the database and reconciliation and validation of bilateral trade data and national statistics in: <strong>Huo, J., Chen, P., Hubacek, K., Zheng, H., Meng, J., & Guan, D. (2022). Full-scale, near real-time multi-regional input–output table for the global emerging economies (EMERGING). Journal of Industrial Ecology, 1– 15. </strong><a href="https://doi.org/10.1111/jiec.13264"><strong>https://doi.org/10.1111/jiec.13264</strong></a></p> <p><strong>EMERGING CO<sub>2</sub> Inventory</strong>: CO<sub>2</sub> emissions (Mt CO<sub>2</sub>eq) from fossil fuel combustion and energy consumption at the sectoral level in each region are available from the IEA database and CEADs emission inventory data. The EMERGING CO<sub>2</sub> Inventory includes a total of 7 energy types: 1. Coal; 2. Natural gas; 3. Oil products; 4. Crude, NGL, Ref Feeds.; 5. Other; 6. Oil shale & oil sands; 7. Peat & Peat products.</p> <div><strong>End year: </strong>The current version of the time series for EMERGING MRIO data is from 2010, 2015 to 2019. We have completed a full update for the period of 2020 to 2022 and the relevant new MRIO table is currently in the testing phase. It will be publicly shared online soon.</div> <div> </div> <p><strong>Announcements</strong></p> <p>These data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights. The availability of these data does not constitute publication of the data. The data providers rely on the ethics and integrity of the user to ensure that they receive fair credit for their work. If the data are obtained for potential use in a publication or presentation, we kindly ask you to cite them properly according to the instructions on the website.</p> <p>Queries about the EMERGING database and further collaboration can be addressed to: jing.j.meng@ucl.ac.uk or huojw20@mails.tsinghua.edu.cn </p>
Trade policies in Central Asia after EAEU enlargement and after Russian WTO accession: regionalism and integration into the world economy revisited
<p>This dataset reproduces empirical results for the paper: Andrzej Cieślik & Oleg Gurshev (2023) Trade policies in Central Asia after EAEU enlargement and after Russian WTO accession: regionalism and integration into the world economy revisited, Eurasian Geography and Economics, DOI: <a href="https://doi.org/10.1080/15387216.2022.2162098">10.1080/15387216.2022.2162098</a></p> <p>It includes data, graphs, and 3SLS gravity analysis performed in the paper. This research was funded in whole by National Science Centre, Poland under PRELUDIUM 20 grant №2021/41/N/HS4/00759. For the purpose of Open Access, the author has applied a CC-BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission.</p>
Data from: Hunting to extinction: biology and regional economy influence extinction risk and the impact of hunting in artiodactyls
Open the record for dataset details and reuse information.
Multi-regional Input–output Table for the Global Emerging Economies (EMERGING V2.5)
<p><strong>EMERGING V2.5</strong>: This version removes the 27th sector, totaling 133 sectors, compared to EMERGING1.0. It has been optimized and calibrated to improve the intermediate input matrix (Z matrix), trade volumes (imports and exports), and total output for the electricity, transportation, and energy sectors across 245 economies, with a particular focus on small and medium-sized emerging economies.</p> <p><strong>EMERGING CO<sub>2</sub> Inventory</strong>: CO<sub>2</sub> emissions (Mt CO<sub>2</sub>eq) from fossil fuel combustion and energy consumption at the sectoral level in each region are available from the IEA database and CEADs emission inventory data. The EMERGING CO<sub>2</sub> Inventory includes a total of 7 energy types: 1. Coal; 2. Natural gas; 3. Oil products; 4. Crude, NGL, Ref Feeds.; 5. Other; 6. Oil shale & oil sands; 7. Peat & Peat products.</p> <p><strong>Announcements</strong></p> <p>These data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights. The availability of these data does not constitute publication of the data. The data providers rely on the ethics and integrity of the user to ensure that they receive fair credit for their work. If the data are obtained for potential use in a publication or presentation, we kindly ask you to cite them properly according to the instructions on the website.</p> <p>Queries about the EMERGING database and further collaboration can be addressed to: huojw20@hotmail.com</p>
Figure 1 from: Neittaanmäki P, Huttula T, Karvanen J, Frisk T, Tuomisto J, Simola A, Tuovinen T, Ropponen J (2016) Unicorn–Open science for assessing environmental state, human health and regional economy. Research Ideas and Outcomes 2: e9232. https://doi.org/10.3897/rio.2.e9232
Figure 1 - Links and interactions between the work packages
Figure 2 from: Neittaanmäki P, Huttula T, Karvanen J, Frisk T, Tuomisto J, Simola A, Tuovinen T, Ropponen J (2016) Unicorn–Open science for assessing environmental state, human health and regional economy. Research Ideas and Outcomes 2: e9232. https://doi.org/10.3897/rio.2.e9232
Figure 2 - Time line of the tasks in UNICORN-project
raw data and code: Climate deterioration and subsistence economy in prehistoric southern Iberia: an evaluation of potential links based on regional trajectories
<p>Here the raw data and R code for reproducing the study 'Climate deterioration and subsistence economy in prehistoric southern Iberia: an evaluation of potential links based on regional trajectories' of Schirrmacher et al. submitted to PlosOne in July 2023 are archived.</p>
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