The role of digital green accounting and environment performance on forest sustainable development goals: A case study on customary forest in Papu
Bibliographic record
Abstract
Management of customary forests through green accounting is an important approach in efforts to achieve Sustainable Development Goals (SDGs). Customary forests, which constitute an important cultural and ecological heritage for local communities, are often threatened by unsustainable exploitation (deforestation) activities. Therefore, Green Accounting is a business concept that focuses on the efficiency and effectiveness of long-term resource use in integrating the customary forest environmental functions and providing social benefits. Therefore, the implementation of green accounting in customary forest management aims to measure and monitor the economic, social and environmental impacts of extractive activities on these forests. This research aims to analyze the relationship between digital green accounting variables on financial performance, environmental performance on sustainable development and digital green accounting towards sustainable development. This research method is quantitative causal which tests the relationship between several variables. The population of this research is indigenous community leaders and the sample of respondents for this research is 432 indigenous community leaders determined using a simple random sampling method. Data analysis for this research uses structural equation modelling (SEM) partial least squares (PLS) with data processing tools using SmartPLS 4.0 software. Research data was obtained by distributing online questionnaires using social media. The independent variables of this research are digital green accounting, environmental performance and the dependent variable is sustainable development. The stages of research data analysis are the outer model test including reliability and validity tests and the inner model test including termination tests and hypothesis tests. Based on the results of data analysis, it is concluded that digital green accounting has a positive and significant relationship to financial performance, environmental performance has a positive and significant relationship to sustainable development and digital green accounting has a positive and significant relationship to sustainable development.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".