METHODOLOGICAL APPROACHES FOR INCLUSION OF FACTORS OF A "GREEN ECONOMY" INTO MEDIUM TERM FORECASTING MODELS FOR REGIONAL DEVELOPMENT
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".