MétaCan
Menu
Back to cohort
Record W3184272683 · doi:10.25115/eea.v39i3.5080

Increasing Forest Cover for a CO2 Neutral Future: Costa Rica Case Study

2021· article· en· W3184272683 on OpenAlexaboutno aff
René Castro Salazar, Rene Castro Cordero, Sarah Cordero Pinchansky

Bibliographic record

VenueStudies of Applied Economics · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPledgePaymentGreenhouse gasQuarter (Canadian coin)BusinessChristian ministryNatural resource economicsOrder (exchange)Direct PaymentsAgricultural economicsEnvironmental protectionEnvironmental scienceGeographyEconomicsFinancePolitical science

Abstract

fetched live from OpenAlex

In the last quarter of 2020 the Costa Rican Ministry for the Environment and Energy (MINAE), announced the signing of two major agreements, the first for US$63 million with the World Bank cooperative program for forest emissions reductions, and the second for US$54 million with the Green Climate Fund (GCF) under the results-based program. These two agreements, are the last of the first-generation of Payment for environmental services (PES), signifying the culmination of a long process that attained a reduction of 14.7 million tC02e for the results-based payment with the GCF and 12 million tCO2e emissions fixed in regenerated forestland and forest fires control with the WB.The first generation of PES was critical in the country´s effort to increase forest cover from the lowest historic point of 21% in 1987 to 57% mark by 2017. There is, however, a new national goal to become CO2 neutral by 2050 as recommended by the Paris agreement since 2015. As a consequence, Costa Rica pledge to further increase forest cover, fixed CO2 in soils and combat forest fires.Costa Rica is one of more than 120 countries committing -under the Paris agreement-to become CO2 neutral by 2050. To achieve this goal each country needs to transform every economic sectors in order to become carbon neutral, either by reducing emissions or by compensating at the national level. However, the country is reaching its maximum capacity to increase forest cover, hence, it needs to consider an integral approach to reach the c-neutral goal. This approach requires to keep generation of electricity based on renewables; to switch transportation from fossil fuels dependency to a cleaner source of energy; and to use every available land to fix as much carbon as possible. That means ensuring the most effective and efficient use of the forest cover that exists today and shifting the focus from a primarily quantitative measurement of forest cover, to a new one that further values the qualitative benefits of species utilized and services produced. In addition, halting deforestation in sensitive areas, increasing forest coverage in areas still available (approximately 3% for forest and 5% for agroforestry) incorporating agroforestry, silvopastoral and multi-use systems that will allow for more sustainable production systems, increasing ecosystem services.Nevertheless, the government’s plan will only succeed if there are negative CO2 emissions from the land use sector (therefore compensating emissions added in other economic sectors); which will require a second-generation of PES (PES 2.0), evolving to an inter sectorial economic instrument. MINAE is currently working on this second-generation instrument. The new PES 2.0 should concentrate in ecosystem services rather than only in forest service and should promote the conversion of land under agricultural production (i.e. cattle and diary) into agroforestry operations. Costa Rica also need to replace the PES funding sources coming mainly from the 1996 fossil fuel tax, ideas such as CO2 trade may provide on future funding option. This paper syntheses government pledges, interviews and the authors’ experience working with the first generation of PES, and presents initial recommendations to increase the effectiveness and impact of PES 2.0.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.414

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.261
Teacher spread0.240 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueStudies of Applied EconomicsSame topicForest Management and PolicyFrench-language works237,207