Energy and environmental impact analysis of rice cultivation and straw management in northern Thailand.

Assessing energy efficiencies and greenhouse gas emissions under bioethanol-oriented paddy rice production in northern Japan

05/02384 Possibility of using waste tire composites reinforced with rice straw as construction materials

A Biological year

Estimating air pollutant emission factors from open burning of rice straw by the residual mass method

Agricultural activities emit about 10-12% greenhouse gases each year. Moreover, the total amount is on the rise, which can significantly contribute to global warming. To get governments to pay more attention to greenhouse gas emissions from agricultural activities, so that the necessary regulations can be implemented. This paper studies the relationship between agricultural land expansion and greenhouse gas emissions. My initial hypothesis was that ceteris paribus, the agricultural land expansion would cause a rise in greenhouse gas emissions. I obtained 25 years of data for 12 countries from the World Development Indicators Database of the World Bank. And created a panel data frame. The variables exist in it are: Total greenhouse gas emissions (kt of CO2 equivalent), Energy use (kg of oil equivalent per capita), Alternative and nuclear energy (% of total energy use), Agricultural land (% of land area), GDP per capita (current US$). In order to cope with the possible heteroscedasticity problem in the regression, several of the variables were processed logarithms. This paper used a Two-way Fixed effect model which controls fixed effects from both cross section and time. The explained variable is log (GHG emissions), and the explanatory variables are log (energy use per capita), log (GDP per capita), clean energy using rate, and percentage of agricultural land in total land. However due to the presence of multicollinearity, log (GDP per capita) was removed. This paper also tested the autocorrelation and used the Cochrane-Orcutt Iterative Process to estimate the autocorrelation coefficient to transform all variables so that solve the autocorrelation issue. The final regression model is as follows: ln(Greenhouse Gases emission)= + 1.06*ln(Energy use per capita) – 0.009*(Alternative and nuclear energy % of total energy use) + 0.0074*(Agricultural land % of land area). = . The overall F-test of this model is significant, and the individual t-tests of the coefficients of each variable are all significant. R2=92.18, adjR2=91.04. This shows that the explanatory power of the model is strong. This outcome is consistent with my initial hypothesis that agricultural land expansion does lead to an increase in greenhouse gas emissions. The other two coefficients are also consistent with our common sense. This result can help governments to decide on the planning of land use. If the current amount of energy used and the types of energy used do not change, a country develops agriculture, it will inevitably lead to more greenhouse gas emissions. More Importantly, it is not like industrial manufacture, normally there are few regulations and policies on agricultural GHG emissions. But the quantitative results of the model tell us that as agriculture expands, it is necessary for the government to implement regulations and policies on it.

As the staple food crop in Sri Lanka, paddy rice occupies around 34% (over 0.87 million hectares of land) of the total arable area in the country, corresponding to an average rice production of 3,774,344 t/year. Rice straw is the major biomass waste from rice cultivation, which approximates to an average of 2,830,758 t/year generation at a theoretical straw/grain ratio of 0.75. Open burning of rice straw in paddy fields is the common practice, which could result in an average GHG emissions of 92 kg CO2 eq/t of dry rice straw and other harmful airborne emissions. Application of rice straw into soil as an organic fertiliser is also an inefficient practice, compared to bioenergy generation using rice straw. The average composition of the Sri Lankan rice straw (i.e. 30.0 wt.% cellulose, 3.9 wt.% hemicellulose, 38.2% lignin, 16.1 wt.% wax, and 12.3 wt.% silica) shows the possibility to be used as a second-generation bioethanol feedstock. Existing studies indicate that bioethanol production from rice straw is more environmentally-benign, compared to alternative options, such as gasification for combined heat and power and dimethyl ether (DME) production. This study analyses the net energy indicators of a possible bioethanol production process from rice straw in Sri Lanka. Chemical process simulations using Aspen Plus software were utilised to evaluate the bioethanol production process from rice straw, with a plant output capacity of 1,000 litres/hr of dehydrated bioethanol (99.7 vol.% ethanol) that can be blended with gasoline as a commercial fuel (e.g. E10: 10% bioethanol+gasoline) without any vehicle engine modification. The cradle to gate bioethanol production process from waste rice straw, considered for net energy analysis consists of three major stages: 1. Rice straw preparation, 2. Rice straw transportation, and 3. Bioethanol conversion. The results show that the considered bioethanol production process has a positive net energy gain and increased renewability factor. Detailed analysis indicates that only around 8% of the total process energy consumption is utilised for the bioethanol dehydration operation that is favourable for converting any existing rice straw ethanol plant into commercial gasohol production plant. The sensitivity of bioethanol yield and process energy parameters for the net energy indicator results are further analysed and discussed. The findings from this study can support decision making for a future waste-to-biofuel plant using waste rice straw in Sri Lanka.Keywords: Rice straw, Bioethanol production, Net energy analysis, Process simulation, Waste-to-biofuel

As the staple food crop in Sri Lanka, paddy rice occupies around 34% (over 0.87 million hectares of land) of the total arable area in the country, corresponding to an average rice production of 3,774,344 t/year. Rice straw is the major biomass waste from rice cultivation, which approximates to an average of 2,830,758 t/year generation at a theoretical straw/grain ratio of 0.75. Open burning of rice straw in paddy fields is the common practice, which could result in an average GHG emissions of 92 kg CO2 eq/t of dry rice straw and other harmful airborne emissions. Application of rice straw into soil as an organic fertiliser is also an inefficient practice, compared to bioenergy generation using rice straw. The average composition of the Sri Lankan rice straw (i.e. 30.0 wt.% cellulose, 3.9 wt.% hemicellulose, 38.2% lignin, 16.1 wt.% wax, and 12.3 wt.% silica) shows the possibility to be used as a second-generation bioethanol feedstock. Existing studies indicate that bioethanol production from rice straw is more environmentally-benign, compared to alternative options, such as gasification for combined heat and power and dimethyl ether (DME) production. This study analyses the net energy indicators of a possible bioethanol production process from rice straw in Sri Lanka. Chemical process simulations using Aspen Plus software were utilised to evaluate the bioethanol production process from rice straw, with a plant output capacity of 1,000 litres/hr of dehydrated bioethanol (99.7 vol.% ethanol) that can be blended with gasoline as a commercial fuel (e.g. E10: 10% bioethanol+gasoline) without any vehicle engine modification. The cradle to gate bioethanol production process from waste rice straw, considered for net energy analysis consists of three major stages: 1. Rice straw preparation, 2. Rice straw transportation, and 3. Bioethanol conversion. The results show that the considered bioethanol production process has a positive net energy gain and increased renewability factor. Detailed analysis indicates that only around 8% of the total process energy consumption is utilised for the bioethanol dehydration operation that is favourable for converting any existing rice straw ethanol plant into commercial gasohol production plant. The sensitivity of bioethanol yield and process energy parameters for the net energy indicator results are further analysed and discussed. The findings from this study can support decision making for a future waste-to-biofuel plant using waste rice straw in Sri Lanka. Keywords: Rice straw, Bioethanol production, Net energy analysis, Process simulation, Waste-to-biofuel

Mid carbon (C6+-C29+) in refractory black carbon aerosols is a potential tracer of open burning of rice straw: Insights from atmospheric observation and emission source studies

Rice straw is one of the most abundant biomass wastes derived from rice cultivation activities. The current rice straw management practice during the wet (rainy) season in Malaysia involves the integration of straw into the soil. This practice offers both advantages and disadvantages to rice farmers and the environment. Straw integration may improve nutrient availability while concurrently causing high greenhouse gas (GHG) emissions due to the increase in soil carbon activity. In this work, the use of microbial substrate to enhance the degradation of straw was compared to an existing technique that used no additional inputs during soil integration. The data collected consisted of overall microbial enzyme production, soil organic carbon, soil nitrogen content, seasonal greenhouse gas emissions, plant characteristics, and crop yield. In brief, these data can be used as means of demonstrating the effects of improved straw degradation during the pre-season on the overall GHG emissions during the planting season.

In this study, a comparative analysis was presented to detect the quota of urban and rural areas from total greenhouse gas (GHG) emissions in 26 selected countries of the Middle East and Central Asia (MECA) during 1994–2014. For this purpose, 18 independent variables such as land area, population characteristics, energy use and consumption, gross domestic product (GDP), CO2 emissions, etc., were considered in addition to one dependent variable of total GHG emissions. Statistical modeling to investigate GHG emissions was constructed comprising the quantitative procedures of the correlation test and clustering analysis, which can be considered as the fundamental basis of each econometric analysis. The GHG emissions from the urban (rural) sector of total countries in 2014 were obtained as 3313.4 (1135.6) Mt of CO2 equivalents, which is about 74.5% (25.5%) of the total GHG emissions (4449.1 Mt of CO2 equivalents) in the MECA region. The correlation test between GHG emissions and urban indicators revealed the significant records (R from 0.745 to 0.981) compared with rural indicators (R from 0.337 to 0.890). Based on the clustering analysis of the countries, Cluster A, comprised of three countries of Iran, Saudi Arabia, and Turkey, was categorized as countries with very high contributing to the total GHG emissions in the MECA region (~ 43.3%). The quotas of emissions from urban and rural sectors in the Cluster A were estimated as 83.1% and 16.9% from the total GHG emissions in 2014 (1921.3 Mt of CO2), while the same quotas were predicted as 73.1% and 26.9% from the total GHG emissions in 2030 (1921.3 Mt of CO2). This study carried out comprehensive research on the GHG emissions from the urban and rural areas in a crucial region of the world, which is faced with the rising growth of population, urbanization, globalization, high-energy use, and fuel consumption.

Integrated emission inventory and modelling to assess the distribution of particulate matters from rice straw open burning in Hanoi, Vietnam

Energy use and greenhouse gas emissions in organic and conventional grain crop production: Accounting for nutrient inflows

The research provided scientific evidences for improved rice straw management. Rice cultivation with in-field burning of rice straw is the worst option with the lowest energy efficiency and highest air pollution emission. This article comprises a comparative assessment of energy efficiency and the environmental footprint of rice production using four different rice straw management scenarios, namely, straw retained, straw burned, partial straw removal, and complete straw removal. Paddy yield, grain quality, and energy balance were assessed for two seasons while greenhouse gas emissions (GHGE) were measured weekly starting from land preparation through to the cropping and fallow period. Despite the added energy requirements in straw collection and transport, the use of collected rice straw for mushroom production can increase the net energy obtained from rice production systems by 10–15% compared to burning straw in the field. Partial and complete removal of rice straw reduces GHGE by 30% and 40% compared to complete straw retention, respectively.

Greenhouse gas (GHG) emissions is one of the major environmental concerns of shale gas development. To better understand this specific environmental impact, this chapter develops a hybrid life cycle inventory (LCI) model to estimate the energy use and greenhouse gas (GHG) emissions of China’s shale gas development. Results suggest a total average energy use per well of 123 TJ (range: 74–165 TJ) and total average GHG emissions per well of 9505 tCO2e (range: 5346–13551 tCO2e). Most of the energy use and GHG emissions are indirect impacts embodied in fuels and materials. Energy use and GHG emissions from the drilling stage comprise the largest share in both totals due to large amounts of diesel used as fuel in the well drilling process and the materials used in the well casing process. Furthermore, the comparison shows that the energy use and GHG emissions of shale gas development in China will be much higher than the U.S.KeywordsShale gas developmentLife-cycle analysisGHG emissionsEnergy useEmbodied energy

BackgroundSocietal pressures exist to reduce greenhouse gas (GHG) emissions from farm animals, especially in beef cattle. Both total GHG and GHG emissions per unit of product decrease as productivity increases. Limitations of previous studies on GHG emissions are that they generally describe feed intake inadequately, assess the consequences of selection on particular traits only, or examine consequences for only part of the production chain. Here, we examine GHG emissions for the whole production chain, with the estimated cost of carbon included as an extra cost on traits in the breeding objective of the production system.MethodsWe examined an example beef production system where economic merit was measured from weaning to slaughter. The estimated cost of the carbon dioxide equivalent (CO2-e) associated with feed intake change is included in the economic values calculated for the breeding objective traits and comes in addition to the cost of the feed associated with trait change. GHG emission effects on the production system are accumulated over the breeding objective traits, and the reduction in GHG emissions is evaluated, for different carbon prices, both for the individual animal and the production system.ResultsMultiple-trait selection in beef cattle can reduce total GHG and GHG emissions per unit of product while increasing economic performance if the cost of feed in the breeding objective is high. When carbon price was $10, $20, $30 and $40/ton CO2-e, selection decreased total GHG emissions by 1.1, 1.6, 2.1 and 2.6% per generation, respectively. When the cost of feed for the breeding objective was low, selection reduced total GHG emissions only if carbon price was high (~ $80/ton CO2-e). Ignoring the costs of GHG emissions when feed cost was low substantially increased emissions (e.g. 4.4% per generation or ~ 8.8% in 10 years).ConclusionsThe ability to reduce GHG emissions in beef cattle depends on the cost of feed in the breeding objective of the production system. Multiple-trait selection will reduce emissions, while improving economic performance, if the cost of feed in the breeding objective is high. If it is low, greater growth will be favoured, leading to an increase in GHG emissions that may be undesirable.