Environmental sustainability assessment of softwood and hardwood seedlings production in forest nurseries: A case study from Pakistan
Bibliographic record
Abstract
This article describes the environmental impacts of producing a single seedling in forest nurseries of selected districts (i.e., Haripur, Abbottabad, and Mansehra) of Hazara Division of Khyber Pakhtunkhwa, Pakistan using the life cycle assessment (LCA) approach. This study was based on the cradle-to-gate approach which begins with the pre-nursery stage and progresses toward the main nursery before transplanting seedlings into the plantation site. Data or life cycle inventory (LCI) of seedling production were collected through questionnaire surveys and personal meetings with forest nurseries managers and workers regarding consumption of different inputs such as electricity, diesel, fertilizers, herbicides, and polyethylene bags, organic manure, and water consumption. The SimaPro software version 8.5 and the CML2000 v2.05 environmental model was applied to perform life cycle impact assessment (LCIA) for a single seedling production in forest nurseries in the study area. In line with the objectives of the study, primary data regarding inputs and outputs of the nurseries were collected from 35 nurseries in the study area by using a random questionnaire method. In addition, secondary data were taken from online databases such as Eco-invent v.3.2 CORRIM and peer-reviewed published literature. For this study, a functional unit of a single seedling was considered. Production weighted average data were modeled in the latest environmental modeling software i.e., SimaPro v.8.5 for ten US-EPA most wanted environmental impacts, such as global warming potential (GWP), abiotic depletion (AD), eutrophication potential (EP), acidification potential (AP), freshwater aquatic eco-toxicity (FAE), marine water eco-toxicity (MWE), terrestrial eco-toxicity (TE), ozone layer depletion (OLD), photochemical oxidation (PO), and human toxicity (HT). The results showed that the highest environmental impact posed by a single seedling was marine aquatic eco-toxicity (11.31360 kg 1,4-DB eq), followed by global warming potential (0.02945 kg CO2 eq) and (0.01227 kg 1,4-DB eq) human toxicity. The primary reason for these environmental burdens was the use of synthetic fertilizers in forest nurseries and the consumption of fossil fuels in nursery mechanization and transportation activities. The total cumulative energy demand for a single seedling was (0.800 MJ) with more than 90% contribution from fossil fuel energy resources such as petrol and diesel. It is therefore highly recommended to use renewable energy resources and organic fertilizers instead of chemical fertilizers in forest nurseries to avoid and minimize greenhouse gas emissions (GHS) and other toxic emissions in the study area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".