农业去碳化:现有和新兴气候智能型做法的温室气体影响和经济效益

IF 4.3 Q2 ENGINEERING, CHEMICAL
Kamila Kazimierczuk*, Sarah E. Barrows*, Mariefel V. Olarte and Nikolla P. Qafoku, 
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引用次数: 0

摘要

全世界都在强调减少温室气体(GHG)排放,这使人们更加关注通过气候智能型农业实践(包括再生、数字和受控环境耕作系统)减少排放的潜力。这些解决方案的有效性在很大程度上取决于其解决环境问题、产生经济回报和满足供应链需求的能力。在本综述中,我们总结了有关这三种现有和新兴农业系统的温室气体影响和盈利能力的知识现状。尽管我们发现这三种方法都有减缓二氧化碳排放的潜力(取决于具体地点和气候因素),但我们指出,再生农业和数字农业在解决非二氧化碳排放(即一氧化二氮和甲烷)方面的研究水平更高,而非二氧化碳排放占农业温室气体足迹的大部分。尽管研究覆盖面有所扩大,但我们仍然发现,在计算这些方法的主要温室气体通量时,尤其是在计算新生的气候智能再生农业方法的终生甲烷足迹时,在方法和数据方面存在很大的局限性。在所探讨的各种方法中,减排的总体效果和持久性仍然存在不确定性--特别是在一氧化二氮排放抵消土壤固碳收益方面,以及与传统系统相比,受控环境农业系统的生命周期排放方面。我们发现,尽管再生农业通常是最容易获得的气候智能方法,但这些方法的经济可行性也因系统而异。强有力的激励措施(包括碳信用考虑因素)、投资和政策变化将使农民在经济上更容易采用这些方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Decarbonization of Agriculture: The Greenhouse Gas Impacts and Economics of Existing and Emerging Climate-Smart Practices

Decarbonization of Agriculture: The Greenhouse Gas Impacts and Economics of Existing and Emerging Climate-Smart Practices

Decarbonization of Agriculture: The Greenhouse Gas Impacts and Economics of Existing and Emerging Climate-Smart Practices

The worldwide emphasis on reducing greenhouse gas (GHG) emissions has increased focus on the potential to mitigate emissions through climate-smart agricultural practices, including regenerative, digital, and controlled environment farming systems. The effectiveness of these solutions largely depends on their ability to address environmental concerns, generate economic returns, and meet supply chain needs. In this Review, we summarize the state of knowledge on the GHG impacts and profitability of these three existing and emerging farming systems. Although we find potential for CO2 mitigation in all three approaches (depending on site-specific and climatic factors), we point to the greater level of research covering the efficacy of regenerative and digital agriculture in tackling non-CO2 emissions (i.e., N2O and CH4), which account for the majority of agriculture’s GHG footprint. Despite this greater research coverage, we still find significant methodological and data limitations in accounting for the major GHG fluxes of these practices, especially the lifetime CH4 footprint of more nascent climate-smart regenerative agriculture practices. Across the approaches explored, uncertainties remain about the overall efficacy and persistence of mitigation─particularly with respect to the offsetting of soil carbon sequestration gains by N2O emissions and the lifecycle emissions of controlled environment agriculture systems compared to traditional systems. We find that the economic feasibility of these practices is also system-specific, although regenerative agriculture is generally the most accessible climate-smart approach. Robust incentives (including carbon credit considerations), investments, and policy changes would make these practices more financially accessible to farmers.

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来源期刊
ACS Engineering Au
ACS Engineering Au 化学工程技术-
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0
期刊介绍: )ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)
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