Purpose Biogas residues, digestates, contain valuable nutrients and are therefore suitable as agricultural fertilizers. However, the application of fertilizers, including digestates, can enhance greenhouse gas (GHG) emissions. In this study different processes and post-treatments of digestates were analyzed with respect to triggered GHG emissions in soil. Methods In an incubation experiment, GHG emissions from two contrasting soils (chernozem and sandy soil) were compared after the application of digestate products sampled from the process chain of a food waste biogas plant: raw substrate, digestate (with and without bentonite addition), digestates after separation of liquid and solid phase and composted solid digestate. In addition, the solid digestate was sampled at another plant. Results The plant, where the solid digestate originated from, and the soil type influenced nitrous oxide (N2O) emissions significantly over the 38-day experiment. Composting lowered N2O emissions after soil application, whereas bentonite addition did not have a significant effect. High peaks of N2O emissions were observed during the first days after application of acidified, liquid fraction of digestate. N2O emissions were strongly correlated to initial ammonium (NH4+) content. Conclusion Fertilization with dewatered digestate (both fractions) increased N2O emission, especially when applied to soils high in nutrients and organic matter.

The goal followed in the present study was to analyze the GreenHouse Gas (GHG) emissions in pasta production. Data for the study was collected from durum wheat production farms from three provinces, Iran. Data of soil properties, transportation as well as and pasta factory production stages were collected and analyzed, using SimaPro Software, Ecoinvent data base and Global Warming Potential (GWP) for 100 year method. EPIC model was used to calculate GHG emissions of land use change. Results indicated the average greenhouse gas emission related to one kg of pasta production was 2.64 kg CO2-eq. Among the different sources of emissions, land use change and fuel consumption for agricultural operations constituted the main contributors to total GWP of pasta production.

The link between economic growth and environmental pollution has been extensively and statistically analyzed over the past two decades. The present study investigates the non-linear effects of variables affecting greenhouse gas emissions. To this aim, a Time Varying Parameters Vector Autoregression (TVP-VAR) model has developed using annual data which covers the period 1972-2018. Moreover, the associated Impulse Response Function (IRF) of the selected influential variables such as GDP (oil, without oil) and electricity consumption on the greenhouse gas emissions (carbon dioxide) has computed. Findings revealed the nonlinear impact of GDP on the greenhouse gas emission, so that until 2002 it had a positive effect on reducing greenhouse gas emission,whereas, from 2002 onwards it had a negative impact and led to escalating the greenhouse gas expansion. Besides, the positive shock of electricity consumption and GDP (without oil) on greenhouse gas emissions during the entire period was positive. Furthermore, the emission of greenhouse gas on itself is positive throughout the study period.

Excessive consumption of energy in Iran resulted greenhouse gas emissions and related risks to the environment and human health. The aim of this study was to evaluate the effects of tillage and cultivation of wheat (including conventional farming, reduced tillage and direct seeding) on energy consumption and environmental pollution in the Kalpoosh region of Semnan province. Data were collected through interviews in 30 typical fields of wheat production. Data were analyzed with regard to inputs consumption, inputs energy and greenhouse gas emissions from the consumed energy and tillage method. Total inputs energy from the consumed inputs varied between 13900 to 18713 MJ ha-1. Results indicated that in conventional method, fuel consumption had the most share of energy input (37.8 % of total) followed by Nitrogen (26.5%) and seed (17.4%), respectively. Nitrogen had the most share of energy input in reduced tillage (31.7%) and direct seeding (35.7%). Maximum and minimum of greenhouse gas emissions (CO2, CH4 and N2O) were related to conventional seeding (11713 kg equivalent CO2 ha-1) and direct seeding (2721 kg equivalent CO2 ha-1.), respectively. Wheat yield differences in three methods wasn’t significant, maximum and minimum of wheat yield related to reduced tillage, direct seeding, respectively. Maximum and minimum of economical productivity belonged to direct seeding (0.21) and conventional tillage (0.16), respectively. It can be concluded that using conservation tillage (reduced tillage and direct seeding), without a significant decrease in the yield of wheat, reduced fuel consumption, energy and greenhouse gas emissions, and increased energy efficiency and economical productivity.

Urban areas are the main sources of greenhouse gas (GHG) emissions. Previous studies have identified the effectiveness of better urban design on mitigating climate change and land-use patterns in cities as important factors in reducing GHG by local governments. However, studies documenting the link between land-use and GHG emissions are scant. Therefore, this study explores the driving forces of land-use change and GHG emission increments in urban areas and investigates their correlations. The study area, Xinzhuang, is a satellite city of Taipei that has rapidly urbanized in the past few decades. Twenty-one potential variables were selected to determine the driving forces of land-use change and GHG emission increments by binomial logistic regression based on the investigation data of national land use in 1996 and 2007. The correlation of land-use change and GHG increments was examined by Spearman rank-order analysis. Results of logistic regression analysis identified that population and its increasing density rate are main driving forces on both land-use change and GHG increments. The Spearman rank correlation matrix indicates that fluctuating urbanization level is significantly correlated with the increase of total GHG emissions, the emissions of residence, commerce, and transportation sectors in neighborhoods; and the emissions of residence and transportation sectors seem closely connected to current urbanization level. The findings suggest that relationships among land-use, urbanization, and GHG emissions in urban areas vary greatly according to residence and transportation characteristics. Land-based mitigation may provide the most viable mechanism for reducing GHG emissions through residence and transportation sectors.

The main causes of global climate change and biodiversity is emission of greenhouse gases from various sources especially from agricultural sector. As one of agricultural hubs Khuzestan has major share in production greenhouse gases with high consumption of chemical fertilizers. Purpose of research present study is applied analytical study that used DAYCENT and DNDC models to determine growth rate of methane, oxidant nitrous and oxidantritic gases in citrus and palm groves of Khuzestan. For this purpose, area was divided into three sections and in each section sampling and determination gas concentration in gardens in each section was performed randomly. Sampling time was done in flowering stage in May 2020 which was peak of green cover for tree, concentration of exhaust gas was done using closed chamber method and gas chromatography in sampling sites. Global warming potential was also obtained based on observational data and DAYCENT and DNDC models. Then efficiency of DAYCENT and DNDC models was investigated using coefficients of determination coefficient, maximum error, root of mean error squares, model efficiency and residual mass coefficient. According to results of DAYCENT model was determined, highest average methane flux modeled in Dezful (ton/hectare in 0. 448) and nitrous oxide flux modeled at Abadan (0. 014 ton/ha per year) and oxidized nitric flux modeled at Dezful (ton/hectare in 0. 152). Also, in DNDC model, highest average of methane flux modeled at Dezful (ton/hectare in 0. 374) oxidized nitrous flux modeled at Abadan (ton/hectare at 0. 258) and oxidative flux modeled at Dezful (A ton per hectare was obtained in 0. 118. Highest global warming potential was determined based on observational data at Abadan (1339. 95 tons equivalent to carbon dioxide) and based on DAYCENT data at Dezful (58. 114 tons equivalent to carbon dioxide) and based on DNDC data at Abadan (111. 669 tons equivalent to carbon dioxide). According to results statistical indicators of DAYCENT model for three gases of oxidenitros, methane and oxidantitric respectively, coefficient of determination (0. 98, 0. 99 and 0. 77) root of mean error squares (0. 05, 0. 31 and 0. 03) and efficiency of model (0. 61, 0. 85 and 0. 76) and in DNDC model coefficients of determination coefficient (0. 98, 0. 98 and 0. 8) root of mean error squares (0. 01, 0. 45 and 0. 04) and efficiency of model (0. 95, 0. 68 and 0. 63) and both models showed acceptable accuracy in estimating said greenhouse gases. It is recommended that instead over-consumption of chemical fertilizers as a major source of greenhouse gas emissions, opinions experts and principles optimal use of fertilizers on farm be used.

Evaluating the relationship between drought events and greenhouse gas emissions from agricultural activities is of particular importance due to their wide-ranging effects on the environment and economy. The purpose of this study is to investigate the relationship between the emission of GHGs resulting from the production of 18 main crops in the form of five dominant categories including; cereals (wheat, barley, rice and maize), legumes (alfalfa, bean, pea and lentil), oil seeds (soybean, sunflower, canola, safflower, castor, sesame and peanut), tubers (potato and sugar beet) and fiber (cotton) with drought indices including; Standard Precipitation Index (SPI), Standard Precipitation Evaporationtranspiration Index (SPEI), Standard Soil Moisture Index of the two upper soil layers (SSI1 and SSI2), Standard multivariable Drought Index including; MSDI1 based on precipitation and reference evapotranspiration (P&ETref), MSDI2 based on precipitation and soil moisture in the first soil layer (P&SM1) and MSDI3 based on precipitation and soil moisture in the second soil layer (P&SM2) in different climates of Iran (coastal wet, mountain, semi mountain, semi desert, desert and coastal desert) during the years 1980-2020. The results showed that the climatic variables of precipitation have no significant relationship with the increase in GHG emissions in different climates of Iran. If the ETref values during the years investigated in this study showed a significant trend. Also, the highest GHG emissions in 2019 were reported as equal to 443 million tons, and the highest GHG emissions were related to electric energy (47.1%) and nitrogen fertilizer (25.75%), respectively. Also, the evaluation of the effectiveness of drought indices for monitoring the effect of climate on GHG emissions showed a better display of SPI, SPEI and MSDI1 indices. The ability to calculate these indices in different time scales makes the effects of drought to be defined more accurately than using indices without this feature.

This study characterized and evaluated the life cycle greenhouse gas (GHG) emissions from different wind electricity generation systems by (a) performing a comprehensive review of the wind electricity generation system life cycle assessment (LCA) studies and (b) statistically evaluating the life cycle GHG emissions (expressed in grams of carbon dioxide equivalent per kilowatt hour, gCO2e/kWh). A categorization index (with unique category codes, formatted as ‘axis of rotation-installed location-power generation capacity’) was adopted for use in this study to characterize the reviewed wind electricity generation systems. The unique category codes were labeled by integrating the names from the three wind power sub-classifications, i.e., the axis of rotation of the wind turbine [horizontal axis wind turbine (HAWT), vertical axis wind turbine (VAWT)], the location of the installation [onshore (ON), offshore (OFF)], and the electricity production capacity [small (S), intermediate (I), large (L)].The characterized wind electricity generation systems were statistically evaluated to assess the reduction in life cycle GHG emissions. A total of five unique categorization codes(HAWT-ON-S, HAWT-ON-I, HAWT-ON-L, HAWTOFF-L, VAWT-ON-S) were designated to the 29 wind electricity generation LCA studies (representing 74 wind system cases) using the proposed categorization index. The mean life cycle GHG emissions resulting from the use of HAWT-ON-S (N=3), HAWT-ON-I (N=4), HAWTON-L (N=58), HAWT-OFF-L (N=8), and VAWT-ONS (N=1) wind electricity generation systems are 38.67, 11.75, 15.98, 12.9, and 46.4 gCO2e/kWh, respectively. The HAWT-ON-I wind electricity generation systems produced the minimum life cycle GHGs than other wind electricity generation systems.