Reply on RC1

This review combines results from a large number of studies investigating energy use in EU open-field agriculture, providing an overview of energy use and its concentrations. Such a review and its findings are important as it informs stakeholders and policymakers with evidence for supporting a green energy transition in open-field agriculture. Our review indicates that annual energy use in EU open-field agriculture is at least 1431 PJ, equivalent to around 3.7% of total EU annual energy consumption, with the majority of energy sourced from non-renewable energy sources. Our meta-analysis finds that the production of fertilizer is the largest energy consuming activity in EU agriculture, accounting for around 50% of all energy inputs. On-farm diesel use accounts for 31% of total energy inputs, while the production pesticides and seeds accounts for 5% of total energy inputs. Other energy uses, mainly irrigation, storage and drying, account for 8% of total energy inputs. This suggests that energy use in EU agriculture is significantly underreported and that around 55% of total energy inputs, associated with the production of fertilizers and pesticides, come from indirect sources which can be assigned to the agricultural sector but is used prior to reaching farms. The importance and potential of various fossil-energy-free technologies and strategies are discussed. In addition, this review highlights that in the medium and long term there is need for the development and application of detailed and standardized methodologies for energy use analysis of agricultural systems, as well as for meta-analyses investigating energy use in agriculture.

Urban residential energy switching in China between 1980 and 2014 prevents 2.2 million premature deaths

The submitted study investigates the role of energy use in agriculture and agricultural output in carbon dioxide emissions with the presence of instrumental variables such as rural population and urbanisation. The data set covers 27 European countries during the period 2010–2020. The quantitative approach was applied using cluster analysis with the previous identification of relations between variables by factor analysis. As the second approach, the Two-Stage Least Square (TSLS) model was estimated. Based on the results, three clusters were created. The heatmap demonstrated the similarity between the comprised countries. The most similar countries are Greece and Hungary, while the most different countries are Luxembourg and Malta. Performed TSLS analysis showed that an increase in energy use is associated with an increase in carbon dioxide emissions. On the other hand, greater agricultural output is associated with lower emissions. However, the statistical significance differs across the individual clusters.

The combustion of woodfuel for residential use is often not considered to be a source of greenhouse gas (GHG) emissions in households since emissions from woodfuel combustion can be offset by the CO2 absorbed by the growth of the forest as a carbon sink (IPCC, 2006). However, this only applies to wood that is harvested in a renewable way, i.e., at a rate not exceeding the regrowth rate of the forest from which it is harvested (Drigo et al., 2002). This paper estimates the share of GHG emissions attributable to non-renewable woodfuel harvesting for use in residential food activities. It adds to a growing research base estimating GHG emissions from across the entire agri-food value chain, from the manufacture of farm inputs, through food supply chains, and finally to waste disposal (Tubiello et al., 2021). Country-level information is generated from United Nations Statistics Division (UNSD) and International Energy Agency (IEA) data on woodfuel use in households. We find that, in 2019, annual emissions from non-renewable woodfuel use in household food consumption were about 745 million tonnes (Mt CO2eq yr−1), with uncertainty ranging from −20 % to + 22 %, having increased 6 % from 1990. Overall, global trends were a result of counterbalancing effects: the emission increases were largely fuelled from countries in Sub-Saharan Africa, Southern Asia, and Latin America while significant decreases were seen in countries in Eastern Asia and South-eastern Asia. The Food and Agriculture Organisation of the United Nations (FAO) has developed and regularly maintains a database covering GHG emissions from the various components of the agri-food sector, including pre- and post-production activities, by country and world regions. The dataset is developed according to International Panel on Climate Change guidelines (IPCC, 2006), which avoids overlaps across AFOLU and energy components. It relies mainly on UNSD Energy Statistics data, which are used as activity data for the calculation of the GHG emissions (Tubiello et al., 2022). The information used in this work is available as open data with DOI https://doi.org/10.5281/zenodo.7310932 (Flammini et al., 2022).

This paper discusses how integrating renewable energy, AI, and IoT becomes important in promoting climate-smart agriculture. Due to the changing climate, rise in energy costs, and ensuring food security, agriculture faces unprecedented challenges; therefore, development toward innovative technologies is emerging for its sustainability and efficiency. This review synthesizes existing literature systematically to identify how AI and IoT could optimize resource management, increase productivity, and reduce greenhouse gas emissions within an agricultural context. Key findings pointed to the importance of managing resources sustainably, the scalability of technologies, and, finally, policy interventions to ensure technology adoption. The paper further outlines trends in the global adoption of renewable energy and smart agriculture solutions, indicating areas of commonality and difference and emphasizing the need for focused policies and capacity-building initiatives that will help, particularly in the developing world, the benefits of such innovations. Eventually, this research covers some gaps in understanding how AI, IoT, and renewable energy could jointly contribute to driving towards a greener and more resilient agriculture sector.

Modern industrial farming technologies have changed substantially in recent decades. In many locations such as Arizona, these industries are highly energy and water-intensive operations. The sustainability of these operations is called into question. The economic, social and climate implications of energy use in agriculture are worth further discussion. Based on the idea of a sustainable energy budget, the production processes of various foods are analyzed which prove to be very energy inefficient. The environmental, ecological and social justice issues of modern industrial agriculture require a reassessment form myriad perspectives: farm subsidies, consumer demand and preferences, engineering technologies, and water policy.

Agriculture sector contributes highly to the GDP of Indonesia. One of the strategies to increase the agricultural production is by accelerating the application of agricultural mechanization. Due to the limitation of manpower in agriculture, the use of agricultural machinery has increased massively since 2014. The index of mechanization has been leveraged from 0.015 in 1983 to 1.68 in 2018. The use of machinery in agriculture will continue to increase, given the adequacy levels of some machinery are still low. The adequacy level of available machinery was estimated at about 49.2% for 2-wheel tractors, 11.9% for 4-wheel tractors, 39.7% for water pumps. This paper estimates the energy use in agriculture by expanding the previous studies of the mechanization index and the adequacy level of agricultural machinery in Indonesia. Currently, the energy in agriculture is under the category of ‘Other Sector’ based on National Statistical Energy Report. Employing available data, this study estimates that the consumption of diesel oil (GasoilCN48) by agricultural machinery is approximately 1.5 million kL or about 82% of particular fuel consumption at ‘Other Sector’ of 2018 Report. Simulating to exponentially increase adequacy level of on-farm machinery (mechanization index up to 4.6 by 2028), the energy consumption of machinery may also grow equal to 3.5 million kL/year. The contribution of energy consumption from agricultural sector can be targeted to intensify the renewable energy application. The renewable energy has been disseminated progressively in agriculture sector. Those are biomass as heating fuel in grain dryers, solar energy to power water pumps, dual fuel (LPG/biogas & gasoline) engines and biodiesel (B30 to B100) for fueling the agricultural machinery with diesel engine. The B100 can be efficiently and effectively used as an alternate fuel for small diesel engines.

Chapter 20 - Social Dimensions of Energy Use in Agriculture

3 - Calculating Energy and Food Production Potential and Requirements

Technology Focus In 2019, the US experienced the lowest natural gas prices since 2016. This was despite natural gas consumption increasing in the residential and commercial sector by 2% (between October and December) according to the US Energy Information Administration (EIA). In July and August, the electric generation sector also experienced an increase in natural gas consumption because of above-average humidity levels in the Midwest and Northeast. Natural gas inventories at the end of March recorded the lowest levels since 2014 during withdrawal season, whereas the injection season recorded the second-highest levels from sustained growth in natural gas production. This trend of low gas prices was because of natural gas production growth, as prices continued to decline through the rest of 2019. The EIA forecasts an increase in natural gas consumption by 1.4 Bcf/D (1.7%) in 2020. The average US natural gas price is projected to fall by 9% to $2.33/million British thermal units (MMBtu) in 2020. The EIA said it expects continued growth in natural gas production in 2020 in response to improved drilling, increased associated gas production, and increased Appalachian and Permian pipeline take-away capacity. As of 3 February, Henry Hub spot price closed at $1.82/MMBtu, $0.87 lower than the same time last year. Weather temperatures were warmer than expected, except in the Northeast and Midwest where it was colder than expected, hence affecting winter demand. Globally, the Paris-based International Energy Agency’s 5-year forecast shows natural gas demand driven by Asia-Pacific growth, with the region accounting for approximately 60% of the total gas consumption forecasted increase through 2024. China is expected to be at the forefront of this demand growth because it is expected to constitute approximately 40% of the total gas demand increase through 2024. The global liquefied petroleum gas (LPG) market has continued to grow reasonably in the past decade, and the US, as a result of its strong domestic LPG production, has ascended in recent years as the largest LPG producer and exporter in the world. The Asia-Pacific region, because of its growing population, constitutes the highest market share of the global LPG consumption. The global LPG market trend is predominantly supply-driven by natural gas and crude oil but not so much from refining. According to IHS Markit, LPG demand in Asia-Pacific grew approximately 3.5% in 2018 and has continued to maintain its steady growth in 2019. Global supplies of LPG are expected to rise approximately 5% to 325 million tonnes in 2020. By region, approximately 28% of the global supplies will be from the US and approximately 20% from the Middle East. Asia-Pacific will continue to be the main driver for LPG demand growth, accounting for approximately 45% (4.6 million B/D) of the global demand. The prominent increase in US LPG exports has resulted in significant changes in the LPG global trade flow patterns in recent years, demonstrating a shift from a dependency on exports from the Middle East. More can be learned by attending the SPE Annual Technical Conference and Exhibition in Denver on 5–7 October and the SPE Asia Pacific Oil and Gas Conference and Exhibition in Perth, Australia, on 8–11 September. Recommended additional reading at OnePetro: www.onepetro.org. SPE 195759 Solutions for Seal Gas Leakage Recovery in Methane Compression: Integration Into Processing Lines or Gas Valorization Systems by Filippo Conforti, Baker Hughes, et al. SPE 197582 Running Sour Hydrocarbon Assets: Eni’s Story of Experience by Luciano Scataglini, Eni, et al. SPE 195555 Experimental Investigation of Integrity Issues of Underground Gas Storage Containing Hydrogen by Erik Clemens Boersheim, Clausthal University of Technology, et al.

This report documents the development and use of the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model. The model, developed in a spreadsheet format, estimates the full fuel-cycle emissions and energy use associated with various transportation fuels for light-duty vehicles. The model calculates fuel-cycle emissions of five criteria pollutants (volatile organic compounds, Co, NOx, SOx, and particulate matter measuring 10 microns or less) and three greenhouse gases (carbon dioxide, methane, and nitrous oxide). The model also calculates the total fuel-cycle energy consumption, fossil fuel consumption, and petroleum consumption using various transportation fuels. The GREET model includes 17 fuel cycles: petroleum to conventional gasoline, reformulated gasoline, clean diesel, liquefied petroleum gas, and electricity via residual oil; natural gas to compressed natural gas, liquefied petroleum gas, methanol, hydrogen, and electricity; coal to electricity; uranium to electricity; renewable energy (hydropower, solar energy, and wind) to electricity; corn, woody biomass, and herbaceous biomass to ethanol; and landfill gases to methanol. This report presents fuel-cycle energy use and emissions for a 2000 model-year car powered by each of the fuels that are produced from the primary energy sources considered in the study.

Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production

Agricultural Energy Use and the Proposed Clean Power Plan

Clean energy farming is the agricultural activity to improve an efficiency of agricultural energy use and to replace fossil fuels. This study was carried out to establish the clean energy farming system in the controlled cultivation of vegetable crop (cucumber) adopting the biogas production facility. In order to design the clean energy farming system, mass and energy balance was analyzed between the controlled cultivation system and the biogas production facility. Net yearly heating energy demands (EYHED) of forcing and semi-forcing cultivation types were 48,697 and 13.536 Mcal 10 -1 in the controlled cultivation of vegetable cucumber. To cover these EYHED, the pig slurry of 511 and 142 m 3 10a -1 (biogas volume of 9,482 and 2,636 Nm 3 10a -1 , respectively, as 60% methane content) were needed in forcing and semi-forcing cultivation types. The pig slurry of 511 m 3 10a -1 caused N 1,788, P2O5 511 kg 10a -1 in the forcing cultivation type, and the pig slurry of 142 m 3 10a -1 caused N 497, P2O5 142 kg 10a -1 in the semi-forcing cultivation type. The daily heating energy demand (Ei,DHED) by the time scale analysis showed the minimum Ei,DHED of 7.7 Mcal 10a -1 day -1 , the maximum Ei,DHED of 515.1 Mcal 10a -1 day -1 , and the mean Ei,DHED of 310.2 in the forcing cultivation type. And the minimum Ei,DHED, the maximum Ei,DHED, and the mean Ei,DHED were 5.3, 258.0, and 165.1 Mcal 10a -1 day -1 in the semi-forcing cultivation type, respectively. Input scale of biogas production facility designed from the mean Ei,DHED were 3.3 and 1.7 m 3 day -1 in the forcing and the semi-forcing cultivation type. The maximum Ei,DHED gave the input scale of 5.4 and 2.7 m 3 day -1 in the forcing and the semi-forcing cultivation type.

Agricultural demands for natural gas and liquefied petroleum gas in the USA