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METHODOLOGICAL APPROACHES FOR INCLUSION OF FACTORS OF A "GREEN ECONOMY" INTO MEDIUM TERM FORECASTING MODELS FOR REGIONAL DEVELOPMENT

2019· article· en· W3012247074 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEconomic innovations · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsCarleton University
Fundersnot available
KeywordsMedium termTerm (time)Inclusion (mineral)EconomicsMacroeconomicsSociologySocial sciencePhysics

Abstract

fetched live from OpenAlex

Topicality. This is based on the importance of coordinating national and regional socio-economic policy with a recognition of the need for “green” growth and an assessment of government policy measures based on the application of multi-regional modeling methodology to analyze the effects of public policy in a regional context, and on medium-term forecasting of a country's sustainable socio-economic development.Aim and tasks. The aim of the study is to improve the scientific validity of methodology for medium-term forecasting of the main parameters of a country's socio-economic development in terms of individual regions by aligning the objectives and priorities of public policy. The objective is to develop, based on a review of international literature, methodological approaches for obtaining coherent medium-term forecast estimates of major groups of territorial economic, social and environmental indicators, based on modern methodologies for measuring the targeted effects of improving living standards, “green” growth, and overall competitiveness of the national economy in its spatial dimension.
 Research results. The results of the research are based on a review of international literature and the justification for methodology to apply modern multi-regional models for the assessment of the effects of interconnected economic, social and environmental policies in the analysis of interactions between national and regional factors of sustainable economic growth, regional disparities, and strengthening of national competitiveness.
 Conclusion. Modern multi-regional models for medium-term forecasting have passed several stages of development, and have incorporated into them theories of the regional economy and the mathematical tools for socio-economic systems modeling. The most effective current policy application is in the practice of recent EU regional policy. Methodology for application of complex multi-regional models has to be flexible, with the application of complementary modeling tools, and providing for further development of model modules to describe the mutual interaction of national and regional factors of sustainable economic growth, including indicators for “green” investment. A number of specific modeling tools (special engineering simulation models, GIS-based models) are usedto assess environmental parameters of spatial development. Our research proposes to incorporate the main indicators of “green” growth into national and regional blocks of multi-regional models, starting with the simplest options such as small econometric models of partial equilibrium, into which - based on a specially conducted analysis - the most significant factors of sustainable economic growth and exogenous parameters of public policy are included. A special place is given to testing the effectiveness of “green” economy measures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.485
GPT teacher head0.388
Teacher spread0.096 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it