A New Sentiment Analysis System of Climate Change for Smart City Governance Based on Deep Learning - Archive ouverte HAL
Communication Dans Un Congrès Année : 2021

A New Sentiment Analysis System of Climate Change for Smart City Governance Based on Deep Learning

Résumé

Unlabelled - The UK Government has set an ambitious target of achieving a national "net-zero" greenhouse gas economy by 2050. Agriculture is arguably placed at the heart of achieving net zero, as it plays a unique role as both a producer of GHG emissions and a sector that has the capacity via land use to capture carbon (C) when managed appropriately, thus reducing the concentration of carbon dioxide (CO) in the atmosphere. Agriculture's importance, particularly in a UK-specific perspective, which is also applicable to many other temperate climate nations globally, is that the majority of land use nationwide is allocated to farming. Here, we present a systematic review based on peer-reviewed literature and relevant "grey" reports to address the question "how can the agricultural sector in the UK reduce, or offset, its direct agricultural emissions at the farm level?" We considered the implications of mitigation measures in terms of food security and import reliance, energy, environmental degradation, and value for money. We identified 52 relevant studies covering major foods produced and consumed in the UK. Our findings indicate that many mitigation measures can indeed contribute to net zero through GHG emissions reduction, offsetting, and bioenergy production, pending their uptake by farmers. While the environmental impacts of mitigation measures were covered well within the reviewed literature, corresponding implications regarding energy, food security, and farmer attitudes towards adoption received scant attention. We also provide an open-access, informative, and comprehensive dataset for agri-environment stakeholders and policymakers to identify the most promising mitigation measures. This research is of critical value to researchers, land managers, and policymakers as an interim guideline resource while more quantitative evidence becomes available through the ongoing lab-, field-, and farm-scale trials which will improve the reliability of agricultural sustainability modelling in the future. Supplementary information - The online version contains supplementary material available at 10.1007/s13593-023-00938-0.
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Dates et versions

hal-03283961 , version 1 (12-07-2021)

Identifiants

Citer

M. Lydiri, Y. El Mourabit, Y. El Habouz. A New Sentiment Analysis System of Climate Change for Smart City Governance Based on Deep Learning. 5th International Conference on Smart City Applications, SCA 2020, Oct 2020, Karabuk, Turkey. pp.17-28, ⟨10.1007/978-3-030-66840-2_2⟩. ⟨hal-03283961⟩
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