Process information
| Key Data Set Information | |
| Location | ND-FJ-CN |
| Geographical representativeness description | The prefecture-level city of Ningde comprises a group of districts and counties, of which Fuding city (A), Xiapu County (B), Fu’an city (C), and Jiaocheng district (D) are next to the sea and were included in our assessment. A total of 321 households were randomly sampled in the area in August 2017 and January 2018, and we analyzed the data from 292 households that had mariculture production during 2016. |
| Reference year | 2017 |
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| Use advice for data set | When utilizing this dataset for Life Cycle Assessment of adult abalone aquaculture, it is important to note that the data reflects practices in the Ningde area and may not be directly applicable to other regions without local calibration. Users should consider the specificity of the data to regional practices, including mariculture infrastructure and household methodologies. Care should be taken when extrapolating this data for different mariculture species or geographic locations. Additionally, users should mind the temporal relevance of the data, collected from families engaged in mariculture in 2016, when evaluating its current applicability. |
| Technical purpose of product or process | This data set represents the process of adult abalone aquaculture in marine cage systems specifically used for the mariculture industry. It details the intensive cultivation of abalone for commercial seafood production while considering the various ancillary processes including energy, feed, other inputs production, and encompasses the main aquaculture production chain. This data is particularly relevant for assessing the environmental impacts in the lifecycle of adult abalone production in coastal areas such as Ningde. |
| Classification |
Class name
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Hierarchy level
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| General comment on data set | The system includes energy production (red), feed production (green), the main chain of aquaculture production (blue), and the production of other inputs (purple). |
| Copyright | No |
| Owner of data set | |
| Quantitative reference | |
| Reference flow(s) | |
| Functional Unit | 1 live-weight ton of abalone |
| Time representativeness | |
| Data set valid until | 2018 |
| Time representativeness description | The literature did not specify the sampling time. The researchers conducted household interviews in August 2017 and January 2018 in the coastal areas of Ningde City |
| Technological representativeness | |
| Technology description including background system | Breeding and culture technology of abalone. The sea area of abalone cultivation must be more convenient for water and land transportation, there is no serious industrial pollution in the sea area, the water flow condition is good, the water depth should not be too shallow, it is best to reach 8 to 15 meters, the water quality is clear, and the transparency is greater than 4 meters; The optimum growth water temperature of abalone is 14~24C, so the water temperature in the sea area should not be less than 10C; The purchase of seedlings should be carried out at the time of optimum temperature for abalone growth. Abalone seedlings should be disinfected with disinfectant before going into the sea. Cage selection |
| Flow diagram(s) or picture(s) | |
| Mathematical model | |
| Model description | In order to account for the variability of the studied system, Monte Carlo simulations were performed in R software (v.3.3.2) to model the correlation between unit process variables in LCA using copula function, which solved the correlation between process variables.Monte Carlo simulations were used in R software, with each cell process represented by a set of input parameters, a correlation matrix of geographical location and growth stages, 2000 iterations were used in the simulation, and the variability of the cell process due to inherent uncertainty, dispersion and unrepresentativeness was considered. Inherent uncertainty is included by assuming a coefficient of variation of 5%, spreads are calculated from primary and secondary data, and a pedigree approach is used to address unrepresentativeness. Dependencies between process chains are included by using dependency sampling in each iteration, as well as dependencies between processes |
Modelling and validation
Administrative information
Inputs and Outputs