Cradle to grave environmental-economic analysis of tea life cycle in Iran

Soheili-Fard, Farshad; Kouchaki-Penchah, Hamed; Raini, Mahmoud Ghasemi Nejad; Chen, Guangnan

doi:10.1016/j.jclepro.2018.06.083

Cited by 41 publications

(20 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Soheili‐Fard et al . studied the environmental impacts of tea production system and they indicated that its environmental impacts could be mitigated by optimization of agro‐chemicals consumption such as chemical fertilizers. However, Iranian urea fertilizer production in 2012 was about 1,282,688 tons (Ministry of Jihad‐e‐Agriculture of Iran, 2013).…”

Section: Resultsmentioning

confidence: 99%

“…Several researchers have reported that the energy consumption of chemical fertilizer was the highest energy consumer during Iranian crop production in general [16,37,52,95-100]. Soheili-Fard et al [101] studied the environmental impacts of tea production system and they indicated that its environmental impacts could be mitigated by optimization of agro-chemicals consumption such as chemical fertilizers. However, Iranian urea fertilizer production in 2012 was about 1,282,688 tons [48] (Ministry of Jihad-e-Agriculture of Iran, 2013).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows

Nikkhah

Rohani

Rosentrater

et al. 2019

Env Prog and Sustain Energy

View full text Add to dashboard Cite

There are some studies regarding the prediction of agricultural energy flows using artificial neural networks (ANNs). These models are quite sensitive to correlations amongst inputs, and, there are often strong correlations amongst energy inputs for agricultural systems. One potential method to remediate this problem is to use principal component analysis (PCA). Therefore, the purpose of this research was to predict energy flows for a specific agricultural system (Iranian tea production) via a novel methodology based on ANNs, and using principal components as model inputs, not raw data. PCA results showed that the first and second components could account for more than 99% of variation in the data, thus the dimensions of the data set could be decreased from six to two for the prediction of energy flows for Iranian tea production. Using these principal components as inputs, an ANN model with 2–15–1 structure was determined to be optimal for energy flow modeling of this system. To conclude, the results of this study highlighted that the use of PC as ANN inputs improved ANN model prediction through reducing its complexity and eliminating data colinearity. Many agricultural systems could benefit from using this methodology for energy modeling. © 2019 American Institute of Chemical Engineers Environ Prog, 38:e13130, 2019

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows

Nikkhah

Rohani

Rosentrater

et al. 2019

Env Prog and Sustain Energy

View full text Add to dashboard Cite

show abstract

“…The analysis of inventory is the second stage of LCA, involving the computation of the amount of each output and input [14] . Based on the functional unit, this stage denotes a comprehensive collection of energy outputs, inputs, materials, along with the emissions to soil, air, and water in all steps of the system [15] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Data supporting midpoint-weighting life cycle assessment and energy forms of cumulative exergy demand for horticultural crops

Mostashari-Rad

Mobtaker

Taki³

et al. 2020

Data in Brief

View full text Add to dashboard Cite

With an increasing demand of horticultural crops, it is critical to examine environmental damages and exergy impacts and evaluate their potential in producing sustainable products of agricultural systems. As such, environmental midpoints of five dominated horticultural products, namely, hazelnut, watermelon, tea, kiwifruit, and citrus, are scrutinized using life cycle assessment approach in Guilan province, Iran. Each crop is considered under a separate scenario and 10 tons of yield is determined as the functional unit. ReCiPe2016, as a new approach, is used for computation of 17 midpoints. Moreover, a weighting analysis is undertaken to find the share of each input in environmental damages with dimensionless notation. In the second part of this paper, cumulative exergy demand (CExD) is applied for evaluation of energy forms in each scenario. Data are presented under two sectors in the main article. The first part is midpoint results of each crop and the second part depicts energy forms of CExD with input rate in each category. Besides, the supplementary files contain raw material of each input, midpoint physical rate, share of each input to contribute midpoint, raw data of weighted damages and share of each input in total weighted damages.

show abstract

“…While tea production is not a GHG-emission-intensive agricultural sector, the scale at which tea is grown and consumed makes it an important focus for GHG emissions reduction. Already a variety of studies have been undertaken examining the energy efficiency, carbon footprint and environmental-economic analysis of tea production in India [14,15], Iran [16][17][18], Sri Lanka [19,20], Malawi [21], Turkey [22] and Kenya [23]. Cichorowski et al [14], Doublet and Jungbluth [15], and Azapagic et al [23] used the life cycle assessment method to analyze the greenhouse gas emissions of tea cultivation and consumption.…”

Section: Introductionmentioning

confidence: 99%

China’s Tea Industry: Net Greenhouse Gas Emissions and Mitigation Potential

et al. 2021

View full text Add to dashboard Cite

Tea is an important cash crop and a beverage that is widely consumed across the world. In China (the largest producer of tea), the industry is growing, and there is a need to understand current greenhouse gas (GHG) emissions and sequestrations and the potential for mitigation so that climate action can be strategically undertaken. Life cycle assessment and carbon footprint methods were used to quantify emissions in tea cultivation and processing in the 16 major producing regions for the year 2017. The system boundary was from cradle to factory gate, which was divided into three subsystems, namely agricultural materials production, tea production and tea processing. Several units of analysis were chosen: the production region (province), the production area (ha) and the product (kg loose tea), etc. Total GHG emissions were 28.75 Mt CO2eq, which were mainly attributable to energy use in tea processing (41%), fertilizer production (31.6%) and soil emissions (26.7%). This equated to 12.0 t CO2eq per ha and 10.8 kg CO2eq per kg processed tea. Production in Hubei, Yunan, Guizhou, Sichuan and Fujian provinces contributed almost two thirds of industry emissions, representing priority areas for strategic action to reduce GHG emissions. At the same time, the total carbon sink amounted to 21.37 MtCO2, representing 74.3% of total GHG emissions. The proportions stored in soil, biomass, and tea production were 49.3%, 30.0%, and 20.7%, respectively. If best recommended management practices for fertilizer application were adopted and biomass was used as a source of energy for tea processing, the GHG emissions reduction potential was 16.66 Mt CO2eq, or 58% of total emissions. The GHG emissions associated with tea production and processing in China appeared high by comparison to other regions of the world. However, considering the carbon sink and emissions reduction potential, the tea industry should be viewed as an important sector for climate action. Moreover, the potential for substantial GHG emissions reduction through the adoption of improved practices seems very realistic. There may also be additional opportunities for GHG emissions reduction through the development of organic tea cultivation systems.

show abstract

Cradle to grave environmental-economic analysis of tea life cycle in Iran

Cited by 41 publications

References 26 publications

Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows

Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows

Data supporting midpoint-weighting life cycle assessment and energy forms of cumulative exergy demand for horticultural crops

China’s Tea Industry: Net Greenhouse Gas Emissions and Mitigation Potential

Contact Info

Product

Resources

About