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Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments' carbon reduction policies can significantly enhance our ability to combat climate change and meet emissions reduction targets. One promising area in this regard is the role of artificial intelligence (AI) in carbon reduction policy and strategy monitoring. While researchers have explored applications of AI on data from various sources, including sensors, satellites, and social media, to identify areas for carbon emissions reduction, AI applications in tracking the effect of governments' carbon reduction plans have been limited. This study presents an AI framework based on long short-term memory (LSTM) and statistical process control (SPC) for the monitoring of variations in carbon emissions, using UK annual CO2 emission (per capita) data, covering a period between 1750 and 2021. This paper used LSTM to develop a surrogate model for the UK's carbon emissions characteristics and behaviours. As observed in our experiments, LSTM has better predictive abilities than ARIMA, Exponential Smoothing and feedforward artificial neural networks (ANN) in predicting CO2 emissions on a yearly prediction horizon. Using the deviation of the recorded emission data from the surrogate process, the variations and trends in these behaviours are then analysed using SPC, specifically Shewhart individual/moving range control charts. The result shows several assignable variations between the mid-1990s and 2021, which correlate with some notable UK government commitments to lower carbon emissions within this period. The framework presented in this paper can help identify periods of significant deviations from a country's normal CO2 emissions, which can potentially result from the government's carbon reduction policies or activities that can alter the amount of CO2 emissions. [ABSTRACT FROM AUTHOR]

The "Global Aridity Index and Potential Evapotranspiration Database - Version 3" (Global-AI_PET_v3) provides high-resolution (30 arc-seconds) global hydro-climatic data averaged (1970–2000) monthly and yearly, based upon the FAO Penman-Monteith Reference Evapotranspiration (ET0) equation. An overview of the methods used to implement the Penman-Monteith equation geospatially and a technical evaluation of the results is provided. Results were compared for technical validation with weather station data from the FAO "CLIMWAT 2.0 for CROPWAT" (ET0: r2 = 0.85; AI: r2 = 0.90) and the U.K. "Climate Research Unit: Time Series v 4.04" (ET0: r2 = 0.89; AI: r2 = 0.83), while showing significant differences to an earlier version of the database. The current version of the Global-AI_PET_v3 supersedes previous versions, showing a higher correlation to real world weather station data. Developed using the generally agreed upon standard methodology for estimation of reference ET0, this database and notably, the accompanying source code, provide a robust tool for a variety of scientific applications in an era of rapidly changing climatic conditions. Measurement(s) evapotranspiration Technology Type(s) Geographic Information System Sample Characteristic - Environment climate system [ABSTRACT FROM AUTHOR]

Drained agricultural peatlands emit significantly higher amounts of greenhouse gas (GHG) emissions per hectare than mineral soils. GHG abatement costs for representative cereals (CF) and dairy (DF) farms in southwestern Finland were estimated by integrating an emission-based tax together with an option to invest in a subsidized adjustable drainage system on peat soils in a farm-level dynamic optimization model. With an average 10% share of peat soils from overall farm area, emissions tax rates over 15 (CF) and 19 (DF) €/tCO2e triggered adjustable drainage investments with a significant reduction in GHG emissions per ha, when assuming no crop-yield effect from the adjustable drainage. Abatement costs for emissions tax rates €12–50/tCO2e/ha were €16–44/tCO2e (CF) and €26–51/tCO2e (DF) for whole farm-soil emissions, depending on the share of peatlands on the farm, on the yield effects of adjustable drainage, and on crop prices. High emissions tax rates imply higher abatement costs since farms have a limited capability to adjust their production and land use. Thus, emissions reductions from peatlands can be achieved at reasonable costs when investing in adjustable drainage on peatlands. The income losses due to emissions tax, however, are high, but they can be compensated for farmers by lumpsum payments independent of their production decisions. Since existing agricultural policies such as the EU CAP system may have limited effectiveness on GHG emissions, the emissions tax and adjustable drainage on peatlands could promote GHG abatement significantly on farms and areas with abundant peatlands. [ABSTRACT FROM AUTHOR]