Environmental management of tea production using joint of life cycle assessment and data envelopment analysis approaches

Abstract

In this study, two methods including data envelopment analysis (DEA) and life cycle assessment (LCA) were applied in order to determine energy efficiency and aid in the reduction of environmental burdens for tea production in Guilan province, Iran. The initial data were collected from 30 tea producers using a face‐to‐face questionnaire. Two models including constant return to scale (CCR) and variable return to scale (BCC) were used for calculating efficiency scores. Simultaneously, ten impact categories (AD, AC, EP, GW, OLD, FE, ME, HT, TE, and PO) were assessed to identify the main problem areas in the tea farms. The results indicated that the average technical efficiency, pure technical efficiency and scale efficiency were 0.881, 0.979, and 0.897, respectively. Moreover, the total energy saving was computed to be in the range of 7193 MJ ha−1. The highest share of total energy saving potential belonged to nitrogen with 65.93% observed. Based on the results, nitrogen, diesel fuel and machinery were the main hotspots in the majority of impact categories in both of present and DEA farms. Furthermore, in this study, the potential for impact category reduction was investigated and the results illustrated that between 17 and 20% of emissions can be reduced by converting present to DEA farms. In conclusion, the joint DEA + LCA method can improve the energy efficiency and environmental impacts in tea production, significantly. © 2017 American Institute of Chemical Engineers Environ Prog, 36: 1116–1122, 2017

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

“…This input was the largest contributor of the total energy consumption, comprising 58.55% (Figure ). Several researchers have reported that the energy consumption of chemical fertilizer was the highest energy consumer during Iranian crop production in general . Soheili‐Fard et al .…”

Environmental and exergetic impacts of cement production: A case study

“…Hazardous emissions related to the diesel consumption are obtained from . Emissions from the cement manufacture process such as: particulate matter (PM and PM‐10), nitrogen oxides (NO x ), sulfur dioxide (SO 2 ), carbon monoxide (CO), carbon dioxide (CO 2 ), and other pollutant such as volatile organic compounds (VOC), ammonia (NH3), chlorine, and hydrogen chloride (HCl) are estimated based on U.S. Environmental Protection Agency emission factors .…”

“…The background system data are collected from the literature and databases. Inventory data for natural gas [18], diesel fuel [19], limestone [20], alluvium [21], silica [22], iron ore [23], production of electricity from natural gas [24], hydropower at reservoir [25], nuclear [26], and raw materials transportation [27] are taken from Ecoinvent 3, ELCD, USLCI, and LCA Food DK Hazardous emissions related to the diesel consumption are obtained from [28,29]. Emissions from the cement manufacture process such as: particulate matter (PM and PM-10), nitrogen oxides (NO x ), sulfur dioxide (SO 2 ), carbon monoxide (CO), carbon dioxide (CO 2 ), and other pollutant such as volatile organic compounds (VOC), ammonia (NH3), chlorine, and hydrogen chloride (HCl) are estimated based on U.S. Environmental Protection Agency emission factors [30].…”

“…Oxides of nitrogen are produced during fuel consumption through oxidation of chemically-bound nitrogen in the fuel and by thermal fixation of nitrogen in the combustion air. Sulfur dioxide may be produced both from the sulfur compounds in the raw materials and from sulfur in the fuel [29]. Regarding to other compounds, the Chloride (Cl) (3.4 kg/ ton cement) is specially originated in the kiln.…”

An integrated analysis of non‐renewable energy use, GHG emissions, carbon efficiency of groundnut sole cropping and groundnut‐bean intercropping agro‐ecosystems

“…The amount of energy used for machinery was calculated by multiplying the time for which the machinery was used by its corresponding energy equivalent. Other inputs, including the diesel fuel, seed (bean and groundnut), biocides and chemical fertilizers used in each production system were then converted into equivalent energy values (MJ ha 21 ) by multiplying the quantity of each input used in the farming systems by their corresponding energy transform coefficient [17,45,46]. Other inputs, including the diesel fuel, seed (bean and groundnut), biocides and chemical fertilizers used in each production system were then converted into equivalent energy values (MJ ha 21 ) by multiplying the quantity of each input used in the farming systems by their corresponding energy transform coefficient [17,45,46].…”